The provided code models a network of neurons using the Wang-Buzsaki model, which is commonly used to simulate spiking neural networks. Here's a breakdown of the biological foundation of key components within the code: ## Neuron Types - **E-cells (Excitatory Neurons):** The network consists of 50 excitatory neurons, represented as "E-cells." These neurons are mainly responsible for transmitting excitatory signals in neural circuits and typically release neurotransmitters like glutamate. - **I-cells (Inhibitory Neurons):** The network includes 20 inhibitory neurons, or "I-cells," which release inhibitory neurotransmitters such as GABA to decrease the likelihood of action potentials in the recipient neuron. ## Membrane Dynamics - **Ionic Currents:** The code includes parameters for ionic currents crucial for action potential generation, such as: - **Sodium (Na⁺) Currents:** Modeled by variables like `gNa` and `ENa`, the sodium conductance and reversal potential, respectively. Excitatory and inhibitory neurons use sodium currents for depolarization. - **Potassium (K⁺) Currents:** The `gK` and `EK` variables model potassium conductance, which is essential for repolarizing the cell after an action potential. - **Calcium (Ca²⁺) Currents:** Represented by `ica`, calcium currents contribute to spike-frequency adaptation and post-spike hyperpolarization (`iahp`). ## Synaptic Transmission - **Synaptic Conductances:** - **gei, gee, gie, gii:** These variables represent conductance strengths between different neuron types (e.g., `gee` for excitatory to excitatory connections). - **Synaptic Inputs:** The input from neighboring neurons is modeled via terms like `inputse` for excitatory input, influencing membrane potential changes. ## Neuromodulation and Spike-Frequency Adaptation - **Calcium-Activated Potassium Currents:** The code models these using variables like `gahp` and `kd`, which facilitate adaptation by modulating afterhyperpolarizations. - **Tonic Input:** The neurons receive a constant "tonic" input represented by `Iapp` to simulate a steady external excitatory drive. ## Gating Variables - **Activation/Inactivation Functions:** These include `Minf`, `Hinf`, and `Ninf`. They represent the fraction of open channels and impact ionic flow, based on Hodgkin-Huxley type formalism. - **Time Constants (tauH, tauN):** These dictate the speed of gating variables' response to changes in the membrane potential. ## Network Dynamics - **Connectivity:** The network has an all-to-all connectivity scheme where each neuron is connected to every other neuron, allowing for complex dynamics and emergent behavior resembling biological neural circuits. Through this code, the model aims to replicate the intrinsic properties of neurons and their interactions, capturing the dynamics of a mixed excitatory-inhibitory neuron network. This is crucial for understanding neural computation, oscillations, and synchronization phenomena in biological neural circuits, particularly in the context of the hippocampus, where the Wang-Buzsaki model is often applied.